Time-scale segmentation of respiratory sounds
Technology and Health Care
Technology and Health Care
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Computers in Biology and Medicine
Elimination of vesicular sounds from pulmonary crackle waveforms
Computer Methods and Programs in Biomedicine
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Multiwavelet neural network and its approximation properties
IEEE Transactions on Neural Networks
Probing the existence of medium pulmonary crackles via model-based clustering
Computers in Biology and Medicine
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Pulmonary crackle detection using time-frequency and time-scale analysis
Digital Signal Processing
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In this study, wavelet networks have been used to parameterize and quantify pulmonary crackles with an aim to depict the waveform with a small set of meaningful parameters. Complex Morlet wavelets are used at the nodes of both single and double-node networks to model the waveforms with the double-node rendering smaller modeling error. The features extracted from the model parameters have been compared with the conventional time domain features in a two-class clustering experiment with nearly 90% matching between the clusters of different parameter sets and with the model parameters forming clusters more closely distributed around their means and better separated from each other. Moreover, using simulated crackles embedded on real respiratory sounds, features extracted from wavelet networks have been shown to be more robust to background vesicular sounds compared to conventional parameters which are very sensitive to noise.